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Abstract Papillary renal cell carcinoma (pRCC) accounts for ~15-20% of the global burden for kidney cancers but still lacks effective mechanisms to predict patient responses to therapeutic intervention. Existing therapies targeting kidney cancers (anti-angiogenesis and immunotherapy) intimately involve the tumor microenvironment in their mechanism of action. However, the role of the physical organization of the tumor in determining therapeutic efficacy remains unknown. Previous efforts have utilized transcriptional profiling to stratify patients into treatment categories but here we propose to leverage artificial neural networks (“AI”) to infer the expression of marker genes for particular cell types of interest based on ubiquitously expressed immunofluorescence channels, unstained brightfield images, and hematoxylin and eosin (H Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 3799.
Brewer et al. (Fri,) studied this question.